Code&Data

All code and scripts are host on GitHub.

Package

itsdm

itsdm calls isolation forest and variations such as SCiForest and EIF to model species distribution. It provides features including:

• A few functions to download environmental variables.
• Outlier tree-based suspicious environmental outliers detection.
• Isolation forest-based environmental suitability modeling.
• Non-spatial response curves of environmental variables.
• Spatial response maps of environmental variables.
• Variable importance analysis.
• Presence-only model evaluation.
• Method to convert predicted suitability to presence-absence map.
• Variable contribution analysis for the target observations.
• Method to analyze the spatial impacts of changing environment.

To install the latest release on CRAN:

install.packages("itsdm")

The latest development version on GitHub can be installed with:

# install.packages("remotes")
remotes::install_github("LLeiSong/itsdm")

hrlcm

This is a package that combines R and Python to do land cover mapping. There are three main parts of scripts for this project:

• data_preprocess: this directory includes all scripts to download and preprocess satellite images.
• guess_model: this directory includes all scripts to build gap-filling Random Forest model and generate ensemble labels.
• hrlcm: this directory includes all scripts to build U-Net model to do land cover mapping.

and other useful scripts and resources:

• tools: other useful scripts for post-mapping processing or use AWS cloud computing.
• docs: useful tutorials or posters/presentations for conference.

To install the hrlcm modeling python package:

git clone git@github.com:LLeiSong/hrlcm.git
cd hrlcm
pip install .

sentinelPot

This package is a package to wrap necessary steps to preprocess sentinel-1&2 images. Full credit should be given to the authors of these preprocessing methods.

Check its GitHub page for more details of how to use it. Note that some functions may be outdated due to the rapid development of image processing.

Sentinel-1

Level-2 process (Use command line graph processing framework in SNAP software):

1. Apply orbit file.
2. Thermal noise removal.
3. Border noise removal.
4. Calibration.
5. Speckle filtering.
6. Range doppler terrain correction.
7. Conversion to dB.

Level-3 process:

1. Apply guided filter to remove speckle noises further.
2. Fit harmonic regression coefficients using Lasso algorithm.

Sentinel-2

Level-2 process:

Basically level-2 means atmospheric correction and cloud/shadow detection. We include three basic methods: sen2cor on SNAP software for atmospheric correction, Fmask for cloud/shadow detection, and MAJA installed on peps server for both. You could find more details here: sen2cor, Fmask and peps MAJA. Here, we combined sen2cor and Fmask together as a regular way for level-2 process, and a MAJA way as a second method.

Level-3 process:

Run docker to process WASP to get seasonal syntheses of sentinel-2 imagery. The details are here: WASP.

Installation

NOTE: You might want to edit requirements.txt before installation if you don’t want to change the existing packages on your own machine.

git clone git@github.com:LLeiSong/sentinelPot.git
cd sentinelPot
pip install .

Other code

waspire

Docker to run Orchestrate WASP (Weighted Average Synthesis Processor) to create monthly syntheses of cloud-free reflectance for sentinel-2 or Venus Level-2A products distributed by the Theia Land data centre.

To install:

1. Download WASP binary zifile, unzip and copy it into the root of this repository.

2. From the root of this repository in terminal, run

   docker build -t wasp .

Dataset

Land cover map of Tanzania (4.77 m) and the training set: OSF.